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PEFT

Fine-tune large language models by training under 1% of parameters with LoRA and QLoRA adapters.

577|62|Updated May 15, 2026
Search Tags:#adapters#lora#transformers#peft#finetuning#qlora
One-click install
npx skills add https://github.com/agentic-in/elephant-agent --skill peft-agentic-in
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: PEFT
Source: https://github.com/agentic-in/elephant-agent/tree/main/packages/skills/builtin_packages/mlops/training/peft
Command: npx skills add https://github.com/agentic-in/elephant-agent --skill peft-agentic-in

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

PEFT addresses the heavy cost of fine-tuning very large language models by updating only a small subset of parameters through adapters such as LoRA and QLoRA, enabling cost-effective customization.

Core Features & Use Cases

  • Memory-efficient fine-tuning by training under 1% of parameters.
  • Support for multiple adapter methods (LoRA, QLoRA, AdaLoRA, IA3, Prefix Tuning, P-Tuning v2) and easy loading/merging of adapters for deployment.
  • Enables multi-adapter serving and model customization across tasks on large models, including deployment via merged adapters.

Quick Start

Install the peft package, configure a LoRA/QLoRA setup for your base model, then begin fine-tuning with your dataset.

Frequently Asked Questions about PEFT

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune large language models on consumer GPUs without running out of memory?▼

You can fine-tune large models on consumer GPUs using parameter-efficient fine-tuning, which trains less than 1% of parameters through adapters like LoRA and QLoRA to drastically reduce memory requirements.

Can I train a 70B model using LoRA adapters and quantization?▼

Yes, parameter-efficient fine-tuning targets large models up to 70B parameters on consumer GPUs by applying quantization and training adapters like LoRA, which updates under 1% of the model weights.

What's the best way to serve multiple customized adapters for different tasks from one base model?▼

Multi-adapter serving allows you to load and serve multiple customized adapters from a single base model, and you can also merge adapters directly into the model for efficient deployment.

What adapter methods are supported for parameter-efficient fine-tuning besides LoRA?▼

Besides LoRA and QLoRA, parameter-efficient fine-tuning supports multiple adapter methods including AdaLoRA, IA3, Prefix Tuning, and P-Tuning v2 for customizing large language models.

How do I configure target modules and training parameters for PEFT?▼

You configure adapters by defining target modules and training parameters directly in code using the peft library, which integrates with the compatible transformers ecosystem to start fine-tuning.

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